# Forecasting at Scale: One Billion (1e9) Time Series with TimeGPT ⚡📈

Imagine you're tasked with forecasting for **one billion unique time series**—ranging from retail sales across thousands of stores to sensor data from millions of IoT devices. It's a monumental challenge, requiring not just statistical modeling but also cutting-edge tools to handle the scale and complexity of the data.

This project is a blueprint for scaling such a task, utilizing **Nixtla's foundation models for time series forecasting** and orchestrating the process efficiently using Python and AWS S3. Here's how you can tackle this kind of project.

## The Challenge 🎯

The goal is simple: forecast the future for **one billion different time series**, but the constraints are anything but simple. How do you handle the storage of this data? 🗄️ How do you parallelize the computation efficiently? 💻 And finally, how do you produce results quickly enough to be useful in decision-making? ⏳

### Enter Foundation Models for Time Series 🚀

**Nixtla** offers **TimeGPT** through an API that leverages foundation models capable of handling large-scale forecasting problems. These models are designed for flexibility and speed 🏎️, making them ideal for scenarios where you're dealing with an enormous volume of data and need results at a high cadence. ⚡

## Results 📊

| 📈 **Number of Series** | Number of Processes | ⏳ **CPU Time (hours)** |
|:-----------------------:|:-------------------:|:------------------:|
| 1e9                     | 1                | 5.5 |
| 1e9 | 5 | 1.1 |

## Running the Project 🛠️

### Installation 🧩

1. Install the required Python packages:
   ```bash
   pip install -r requirements.txt
   ```

2. Configure AWS credentials so the script can interact with S3:
   ```bash
   aws configure
   ```

### Usage 🏃‍♂️

To generate forecasts, you simply run the following command. Adjust the parameters as needed:

```bash
python main.py --bucket <your-bucket-name> --prefix <your-s3-prefix> --n_partitions 1000 --series_per_partition 1000000 --n_jobs 5
```

- **`bucket`**: The S3 bucket where the data is stored.
- **`prefix`**: The path inside the S3 bucket where the input and output data is stored.
- **`n_partitions`**: The number of partitions to break the task into.
- **`series_per_partition`**: The number of time series in each partition.
- **`n_jobs`**: The number of processes to run in parallel.

### What Happens Behind the Scenes 🔍

The code will:

1. Check if the forecast for each partition has already been generated. ✅
2. Generate new time series data for each partition. 🧬
3. Use Nixtla’s API to compute forecasts for each partition. 🔮
4. Save the results and the time taken to S3. 💾

## Scaling to Billions 🚀

This approach is designed to **scale**—whether you’re forecasting for **one million** or **one billion** series. By partitioning the data, processing it in parallel 🧠, and leveraging foundation models like those provided by Nixtla, you can handle even the most massive forecasting tasks efficiently. ⚙️

### Final Thoughts 💡

Forecasting at scale is no easy feat, but with the right tools, it’s entirely achievable. This project demonstrates how modern time series forecasting techniques can be applied to massive datasets in an efficient, scalable way. By leveraging AWS infrastructure, foundation models, and clever parallel processing, you can forecast the future for billions of unique data series—**unlocking insights** that can power decision-making at an unprecedented scale. 🌍✨
